Multiplayer reach-avoid differential games with simple motions: A review

Abstract

This paper reviews the recent works on multiplayer reach-avoid (M-RA) differential games between two adversarial teams in a game region which is split into a goal region and a play region. The pursuit team aims to protect the goal region from the evasion team by cooperatively capturing the evaders which start from the play region and strive to enter the goal region. We provide a selective overview of algorithms and theoretical results for multiplayer reach-avoid differential games. Specifically, we focus on point mass holonomic players that can move freely in the game region and have simple motions as Rufus Isaacs states. We describe how the challenges due to high-dimensional continuous joint action and state spaces, as well as complex cooperations and competitions among players, can be properly resolved by a combination of qualitative and quantitative analysis of small-scale games and optimal task allocation. We finally point out the limitations of the current works and identify fruitful future research directions on theoretical studies of multiplayer reach-avoid differential games.

Matching-based capture strategies for 3D heterogeneous multiplayer reach-avoid differential games

“…We split the whole game into many subgames, compute strategies for each subgame separately and generate team strategies by piecing together all subgame outcomes. This approach has been widely used to solve multiplayer games [6], [13], [33], [39], [40]. Notably, the very relevant work [6] by Chen et al only considered onepursuer-one-evader subgames and solved multiplayer games 1.…”

“…We propose a hierarchical-matching receding-horizon cooperative pursuit strategy that is computationally efficient and can achieve a continuously improving lower bound on the number of evaders that can be defeated. Our study is motivated by the recent popularity of reach-avoid differential games [6], [13], [22], [24], [29], [33], [36], [39],…”

“…Most of the works on reach-avoid differential games assume obstacle-free environments, that is, the players can move freely in the environment [7], [30], [33], [34], [36], [37], [39]- [43]. However, the addition of obstacles enables the study of more complex and realistic scenarios [18].…”

“…A player can win if the game starts from a state in its own winning region, despite the opponent's strategy. As far as we know, although the winning regions for reach-avoid differential games without obstacles, have been treated in the literature [9], [13], [33], [39]- [43], an efficient approach for computing winning regions and strategies for environments with obstacles is missing, and it becomes even harder for multiplayer games.…”

Transfer reinforcement learning for multi‐agent pursuit‐evasion differential game with obstacles in a continuous environment

“…The pursuit-evasion game (PEG) involves two opposing groups: the pursuers and the evaders. It primarily focuses on how pursuers cooperate to capture evaders and the strategies that evaders should adopt to avoid capture or prolong the capture time [1][2][3][4]. In recent years, multi-agent PEG has been widely applied in both military and civil fields, such as missile defense and interception [5,6], air combat [7], search and rescue [8,9], and transportation management [10].…”